Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation

Leukemia (a cancer of leukocytes) basically develops in the bone marrow. Acute myelogenous leukemia (a type of leukemia) has eight sub-types according to French-American-British classification. These forms can be visually observed by pathologists using microscopic images of infected cells. However, identification task is tedious and usually difficult due to varying features. Automatic leukemia detection is an important topic in the domain of cancer diagnosis. This paper presents a novel method based on dictionary learning and sparse representation for detecting and classification of different sub-types of AML. For each class, two intensity and label dictionaries are designed for representation using image patches of training samples. New image is represented by all dictionaries and the one with minimum error determine the type of class. We considered M2, M3 and M5 sub-types for evaluation of the method. The initial implementing of the proposed method achieved 97.53% average accuracy for different sub-types of AML.